1. Field of the Invention
The invention relates to an adaptive signal processing method for bidirectional radio transmissions of digital data streams in uplink and downlink transmission directions over a MIMO-channel with n antennae at one side of the channel and m antennae at the other side of the channel and with signal preprocessing of the transmission signals x at the transmission side and signal post processing of received signals y at the receiver both of which are based upon an estimate of the complex-valued channel matrix, and to a MIMO-system with at least one uplink transmission station with n antennae, a downlink transmission station with m antennae, as well as to a signal processing unit at least in the station provided with m antennae for executing the adaptive signal processing method.
2. The Prior Art
Mobile communications systems are expected in narrow frequency bands and at low transmission power to transmit at as low an error rate as possible, data of a high bit rate. This ideal is opposed by the arbitrary nature of the radio channel the amplitude and phase Gang of which change significantly in time, locale and as a function of carrier frequency (fading). Statistically, radio channels can be modulated by a Rayleigh (without visual connection) or Rice distribution (with visual connection). The invention relates to an adaptive transmission method on the basis of multi-element antennae at a mobile station at one and a base station on the other channel side between which bidirectional radio operation is taking place so that both sides may act as transmitter and receiver. Such “multiple-input-multiple-output” (MIMO) systems are being increasingly investigated on a worldwide basis because with them the quantity of data which can be transmitted per 1 Hz of bandwidth (“spectral efficiency”) can be significantly increased. This is achieved by the simultaneous transmission of several data streams in the same frequency band (same channel operation).
MIMO-systems have assumed an important role in radio transmission ever since Foschini demonstrated that with them the spectral efficiency, i.e. the use of the scarce resource “bandwidth” can be significantly improved [1]. In a MIMO-system several digital data streams are simultaneously transmitted at the same frequency by several transmission antennae (transmission vector x). The several receiving antennae (receiving vector y) test the resulting field distribution at several localities, i.e. there is always a different superposition of the transmission signals. This may be described by a vectorial equationy=H·x+n  (1).
H represents the so-called “channel matrix” in which the values of amplitude and phase of the individual channels are listed between every possible pair of transmission and receiving antennae (data signal paths). Vector n describes the noise at the individual receiving antennae. At the receiving side, the transmitted data signals can be separated again from each other by appropriate signal processing. In order to separate the data signals at the receiving side, knowledge of the channel matrix H is necessary. The knowledge may in practice be obtained by a repeated estimate of the channel matrix H, made in advance and at regular intervals, on the basis of reference signals [1].
In very simple MIMO-systems of the prior art, a special pseudo inverse H−1 of H (for instance the Moore-Penrose-pseudo inverse) is initially calculated from the knowledge of a channel matrix of dimension n×m and the individual listings hij in the channel matrix (each listing being a complex number describing the transmission in the radio channel from the jth transmission antenna to the ith receiving antenna), followed by a multiplication therewith of the receiving vector (so-called “zero-forcing”) for reconstruction of the transmitted data signals (reconstructed data signals marked by an apostrophe). Thus:x′=H−1·y=x+H−1·n  (2).
In this purely receiving-side signal processing, the term (H−1·n) is problematic. Because of it, during decorrelation of the transmitted signals in accordance with equation (2), the noise in the individual data signal paths is also amplified as a function of the channel. Consequently, the transmission power required for an error-free decoding of all data signals is relatively high.
A marked improvement was brought about by the work of Golden, Foschini et al. 1999, proposing recursive signal processing at the receiver (Bell Labs Layered Space-Time, “BLAST” or “V-BLAST” [2]. In accordance with it, the strongest received signal is initially selected and decoded. Prior to detection of the next strongest signal, the signal which has already been received is subtracted from the receiving signals of all antennae. In this manner, the effective number of transmitters in the system is reduced, i.e., progressively fewer transmitters must be detected with the same number of receiving antennae which reduces the probability of errors in the detection of the remaining data streams. To this end, the corresponding column in the channel matrix is eliminated and a modified pseudo inverse H1−1 is calculate. The norm of the line vectors in the matrices H1−1 (I=1 . . . n) is reduced by the surplus of receiving antennae as they increase step by step. In this manner, the noise variance becomes smaller, and the error rate in the detection of the remaining data signals is lower. Overall, the bit error rate is significantly improved by the BLAST method. Several variations of the recursive signal processing method exist (e. turbo-BLAST [3] or BLAST in combination with MMSE-detection [4]). Their weak points reside in the time-consuming recursive processing of the signals which renders any real-time transmission at high data rates difficult, as well as in the non-linear decisions executed within the recursion which in case of an error may lead to further erroneous decisions in subsequently detected data streams. This error progression occurs in many non-linear detection algorithms, e.g. in the so-called “decision feedback equalizer” (DFE). While the BLAST method may thus reduce the effect of the term (H−1·n) of equation (2), it cannot eliminate it.
In principle, it has been known that it is possible to improve the efficiency of the system by signal processing at the transmission side. For instance, in the paper by Teletar, 1999 [5] a condition is reported of how knowledge of the channel allows maximization of the capacity at the transmission side. The method is called “water filling method”. However, for optimizing the theoretical information capacity no direct reference is made in the report in respect of the modulation process to be employed and the number of the actually used individual channels. In the water filling method, bad channels are switched off because of the advantage of transmitting their data signals at a higher data rate over the remaining channels. In this connection, the transmission power is distributed over the remaining channels such that in all channels the sum of transmission and noise power is equal to a constant which may figuratively be interpreted as “water filling”. However, tests have shown that the water filling method yields a noticeable increase in capacity only at a low signal-to-noise-ratio. It does, however, require a comparatively high flexibility and complexity in its transmission system in view of the fact that the signal-to-noise-ratio in individual channels differs. Accordingly, a correspondingly adjusted modulation and coding process is required in each individual channel. Telatar's theoretical work proposes this approach for improving the capacity of a MIMO-system. For evaluating communications systems, the information theory utilizes the capacity as a measure for the highest possible quantity of data which can be transmitted at a low error rate in a bandwidth of 1 Hz. However, when considering capacity, marginal technical conditions such as, for example, data rates, modulation methods and channel coding are ignored. From Shannon's deduction it becomes apparent that the ultimate capacity can only be achieved at a redundance in the system approaching infinity which is equal to an infinitely high technical complexity. However, this renders the efficiency of applying this system in practice very small. Hence, in practical applications it is usually the bit error rate which is used for systems evaluation where the technical parameters mentioned above are recorded. Transmission processes are then selected which in respect of capacity are always sub-optimal but which are optimal in respect of the efficiency of a practical application. As a rule, such processes are of very simple structure or they make use of certain conditions in the transmission channel in order markedly to reduce the technical complexity.
The above-described “zero forcing”, “V-BLAST” and “water filling” processes are intended for so-called “flat MIMO-channels”. A channel may be considered to be “flat” if the complex valued channel coefficient changes but little as a function of the frequency in the transmission band. This is only the case in narrow-banded transmission systems where the propagation scattering of the transmission signals between transmitter and receiver are smaller than the symbol duration. Otherwise, the channel is frequency-selective, and a chronological regeneration of the signals is additionally required. In the paper by Wong et al., 2000 [6] the common space-time regeneration for a MIMO-system in frequency-selective channels is investigated. The data to be transmitted are multiplied by a transmission matrix T and the data to be received are multiplied by a receiving matrix R. A conventional signal model is being used in which the folding of the transmission signal otherwise necessary is reformulated with the matrix channel pulse response as multiplication by a Toeplitz-matrix. In the paper, that structure of the R and T matrices is deduced which minimizes the signal-to-interference-and-noise-ratio at the detector (minimum means square error solution, MMSE). Thereafter, the complete solution is reduced for the frequency-selective channel for the case of a flat channel. It is not clear, however, how, in an actual systems approach, the transmitter can obtain knowledge about the channel coefficients. In frequency-selective channels the number of channel parameters to be estimated increases by a the factor of the memory length of the channel. When looking at a MIMO-system, for instance, of 8 transmitters and 12 receivers at a memory length of the channel of 10 symbols, the transmitter has to know 8×12×10=960 complex parameters to carry out the operations at the transmitter described by Wong et al. Since the channel may change in time, these parameters must be retransmitted from the receiver to the transmitter within the very short coherence time of the channel (e.g. 5 ms). At a resolution of 8 bits for the real and imaginary component of each channel coefficient, a bandwidth of 2*960*8 bit/5 ms≈3 Mbit/s is required in the return channel for only the return transmission of the channel information. This does not appear to be practicable. It remains open, furthermore, whether the method described by Wong et al. is suitable for any antenna configurations, and which requirements exist as regards the exactness of the channel estimation.